It would not say how many events are flagged, or how many of those yield evidence of misbehaviour.

The "machine learning" software it is developing will be able to look beyond those set patterns and understand which situations truly warrant red flags, Gira said.

In the case of market surveillance, machine learning would mean the computers "learn" which trading patterns lead to enforcement charges, in order to flag the right ones.

FINRA plans to test the new tool next year alongside its existing systems to compare the results.

The regulator has already moved its surveillance systems to the Amazon cloud, giving it more computing power to quickly analyse massive amounts of data.

Nasdaq is working with cognitive computing firm Digital Reasoning, which it invested in earlier this year.

LSE has teamed up with IBM Watson business and cyber-security firm SparkCognition to develop its AI-enhanced surveillance, Chris Corrado, chief operating officer of LSE Group said.

Trader integrity

The technology would not necessarily prevent events such as the 2010 "flash crash," when the Dow Jones Industrial Average temporarily plunged more than 1000 points.

However, it could be quicker to catch manipulative behaviour thought to contribute to them, potentially saving market watchdogs time and money.

FINRA, Nasdaq and LSE would not provide specific figures for how much the software costs to develop or how much money they expect it to save.

For instance, investigators spent years cross-referencing trading data with old electronic communications to make their case against a group of global banks whose traders were rigging foreign exchange benchmarks.

Nasdaq said the software it is testing with Digital Reasoning and other financial firms could do that task almost in real time.

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